I’ve written a tool (ipython_memory_usage) to help my colleague and I understand how RAM is allocated for large matrix work, it’ll work for any large memory allocations (numpy or regular Python or whatever) and the allocs/deallocs are reported after every command. Here’s an example – we make a matrix of 10,000,000 elements costing 76MB and then delete it:
IPython 2.1.0 -- An enhanced Interactive Python. In : %run -i ipython_memory_usage.py In : a=np.ones(1e7) 'a=np.ones(1e7)' used 76.2305 MiB RAM in 0.32s, peaked 0.00 MiB above current, total RAM usage 125.61 MiB In : del a 'del a' used -76.2031 MiB RAM in 0.10s, peaked 0.00 MiB above current, total RAM usage 49.40 MiB
UPDATE As of October 2014 I’ll be teaching High Performance Python and Data Science in London, sign-up here to get on our course announce list (no spam, just occasional updates about upcoming courses). We’ll cover topics like this one from beginners to advanced, using Python, to do interestinng science and to give you the edge.
The more interesting behaviour is to check the intermediate RAM usage during an operation. In the following example we’ve got 3 arrays costing approx. 760MB each, they assign the result to a fourth array, overall the operation adds the cost of a temporary fifth array which would be invisible to the end user if they’re not aware of the use of temporaries in the background:
In : a=np.ones(1e8); b=np.ones(1e8); c=np.ones(1e8) 'a=np.ones(1e8); b=np.ones(1e8); c=np.ones(1e8)' used 2288.8750 MiB RAM in 1.02s, peaked 0.00 MiB above current, total RAM usage 2338.06 MiB In : d=a*b+c 'd=a*b+c' used 762.9453 MiB RAM in 0.91s, peaked 667.91 MiB above current, total RAM usage 3101.01 MiB
If you’re running out of RAM when you work with large datasets in IPython, this tool should give you a clue as to where your RAM is being used.
UPDATE – this works in IPython for PyPy too and so we can show off their homogeneous memory optimisation:
# CPython 2.7 In : l=range(int(1e8)) 'l=range(int(1e8))' used 3107.5117 MiB RAM in 2.18s, peaked 0.00 MiB above current, total RAM usage 3157.91 MiB
And the same in PyPy:
# IPython with PyPy 2.7 In : l=[x for x in range(int(1e8))] 'l=[x for x in range(int(1e8))]' used 763.2031 MiB RAM in 9.88s, peaked 0.00 MiB above current, total RAM usage 815.09 MiB
If we then add a non-homogenous type (e.g. adding None to the ints) then it gets converted back to a list of regular Python (heavy-weight) objects:
In : l.append(None) 'l.append(None)' used 3850.1680 MiB RAM in 8.16s, peaked 0.00 MiB above current, total RAM usage 4667.53 MiB
The inspiration for this tool came from a chat with my colleague where we were discussing the memory usage techniques I discussed in my new High Performance Python book and I realised that what we needed was a lighter-weight tool that just ran in the background.
My colleague was fighting a scikit-learn feature matrix scaling problem where all the intermediate objects that lead to a binarised matrix took >6GB on his 6GB laptop. As a result I wrote this tool (no, it isn’t in the book, I only wrote this last Saturday!). During discussion (and later validated with the tool) we got his allocation to <4GB so it ran without a hitch on his laptop.
UPDATE UPDATE excitedly I’ll note (and this will be definitely exciting to about 5 other people too including at least @FrancescAlted) that I’ve added proto-perf-stat integration to track cache misses and stalled CPU cycles (whilst waiting for RAM to be transferred to the caches), to observe which operations cause poor cache performance. This lives in a second version of the script (same github repo, see the README for notes). I’ve also experimented with viewing how NumExpr makes far more efficient use of the cache compared to regular Python.
I’m probably going to demo this at a future PyDataLondon meetup.
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.